Deepfakes: trick or treat?

Kietzmann, J., Lee, L. ORCID: 0000-0002-3818-4445, McCarthy, I. and Kietzmann, T., 2019. Deepfakes: trick or treat? Business Horizons. ISSN 0007-6813

1247050_Lee.pdf - Post-print

Download (1MB) | Preview


Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio and video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.

Item Type: Journal article
Publication Title: Business Horizons
Creators: Kietzmann, J., Lee, L., McCarthy, I. and Kietzmann, T.
Publisher: Elsevier
Date: 24 December 2019
ISSN: 0007-6813
Divisions: Schools > Nottingham Business School
Record created by: Linda Sullivan
Date Added: 03 Dec 2019 16:24
Last Modified: 24 Dec 2022 03:00

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year